code: def get_facet_type_from_numpy_type(dtype):
      """Converts a Numpy dtype to the FeatureNameStatistics.Type proto enum."""
      fs_proto = facet_feature_statistics_pb2.FeatureNameStatistics
      if dtype.char in np.typecodes["Complex"]:
          raise MlflowException(
              "Found type complex, but expected one of: int, long, float, string, bool"
          )
      elif dtype.char in np.typecodes["AllFloat"]:
          return fs_proto.FLOAT
      elif (
          dtype.char in np.typecodes["AllInteger"]
          or np.issubdtype(dtype, np.datetime64)
          or np.issubdtype(dtype, np.timedelta64)
      ):
          return fs_proto.INT
      else:
          return fs_proto.STRING
  
file_path: mlflow/mlflow/recipes/cards/pandas_renderer.py
imports: import base64
  import sys
  from typing import Iterable, Tuple, Union
  import numpy
  import pandas
  from packaging.version import Version
  from mlflow.exceptions import MlflowException
  from mlflow.protos import facet_feature_statistics_pb2
  from mlflow.recipes.cards import histogram_generator
label_desc: Use image node_id 1 for calling a global function with example usage: get_facet_type_from_numpy_type(dtype) and returns: fs_proto, fs_proto, fs_proto